Evidence map›Paper›PMID 20544033›Full record

ArticlePloS one2010

Simplified automated image analysis for detection and phenotyping of Mycobacterium tuberculosis on porous supports by monitoring growing microcolonies.

Alice L den Hertog, Dennis W Visser, Colin J Ingham, Frank H A G Fey, Paul R Klatser, Richard M Anthony

Abstract read
In one paragraph

Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. ScanLag: high-throughput quantification of colony growth and lag time.Journal of visualized experiments : JoVE · 2014
    Article
  8. Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Alice L den HertogKIT Biomedical Research, Royal Tropical Institute, Amsterdam, The Netherlands. a.d.hertog@kit.nl
Dennis W Visser
Colin J Ingham
Frank H A G Fey
Paul R Klatser
Richard M Anthony

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEven with the advent of nucleic acid (NA) amplification technologies the culture of mycobacteria for diagnostic and other applications remains of critical importance. Notably microscopic observed drug susceptibility testing (MODS), as opposed to traditional culture on solid media or automated liquid culture, has shown potential to both speed up and increase the provision of mycobacterial culture in high burden settings.

methodsHere we explore the growth of Mycobacterial tuberculosis microcolonies, imaged by automated digital microscopy, cultured on a porous aluminium oxide (PAO) supports. Repeated imaging during colony growth greatly simplifies "computer vision" and presumptive identification of microcolonies was achieved here using existing publically available algorithms. Our system thus allows the growth of individual microcolonies to be monitored and critically, also to change the media during the growth phase without disrupting the microcolonies. Transfer of identified microcolonies onto selective media allowed us, within 1-2 bacterial generations, to rapidly detect the drug susceptibility of individual microcolonies, eliminating the need for time consuming subculturing or the inoculation of multiple parallel cultures. SIGNIFICANCE: Monitoring the phenotype of individual microcolonies as they grow has immense potential for research, screening, and ultimately M. tuberculosis diagnostic applications. The method described is particularly appealing with respect to speed and automation.

Indexed as

AutomationCulture MediaMycobacterium tuberculosisCulture Media

Identifiers

PMID20544033
PMCPMC2882339

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.